relative_eff() computes the the MCMC effective sample size divided by
the total sample size.
relative_eff(x, ...) # S3 method for default relative_eff(x, chain_id, ...) # S3 method for matrix relative_eff(x, chain_id, ..., cores = getOption("mc.cores", 1)) # S3 method for array relative_eff(x, ..., cores = getOption("mc.cores", 1)) # S3 method for `function` relative_eff( x, chain_id, ..., cores = getOption("mc.cores", 1), data = NULL, draws = NULL ) # S3 method for importance_sampling relative_eff(x, ...)
A vector, matrix, 3-D array, or function. See the
Methods (by class) section below for details on specifying
A vector of length
The number of cores to use for parallelization.
|data, draws, ...||
Same as for the
A vector of relative effective sample sizes.
default: A vector of length \(S\) (posterior sample size).
matrix: An \(S\) by \(N\) matrix, where \(S\) is the size
of the posterior sample (with all chains merged) and \(N\) is the number
of data points.
array: An \(I\) by \(C\) by \(N\) array, where \(I\)
is the number of MCMC iterations per chain, \(C\) is the number of
chains, and \(N\) is the number of data points.
function: A function
f() that takes arguments
draws and returns a
vector containing the log-likelihood for a single observation
at each posterior draw. The function should be written such that, for each
f(data_i = data[i,, drop=FALSE], draws = draws)
results in a vector of length
S (size of posterior sample). The
log-likelihood function can also have additional arguments but
draws are required.
If using the function method then the arguments
draws must also
be specified in the call to
data: A data frame or matrix containing the data (e.g.
observed outcome and predictors) needed to compute the pointwise
log-likelihood. For each observation
ith row of
data will be passed to the
data_i argument of the
draws: An object containing the posterior draws for any
parameters needed to compute the pointwise log-likelihood. Unlike
data, which is indexed by observation, for each observation the
draws will be passed to the
draws argument of
the log-likelihood function.
... can be used if your log-likelihood function takes additional
arguments. These arguments are used like the
draws argument in that they
are recycled for each observation.
x is an object of class
relative_eff() simply returns
r_eff attribute of